The detection and tracking of a moving target has always been a core technical issue studied in computer vision, pattern recognition and other fields. The key to the detection and tracking of the moving target is to use a computer vision technique and a pattern recognition classification method to detect the moving target in a video sequence image, and to effectively and stably track a target area. For example, in an intelligent transportation system, an accident vehicle may be automatically tracked and monitored by tracking the moving target; in home intelligent entertainment equipment, the automatic tracking of a moving human body may be controlled; in a military field, a weapon may be precisely guided, and so on.
When the present computer vision technique is used to identify and position a target and analyze the movement of the target, it is necessary to first perceive a surrounding environment, obtain depth information, establish a 2D or 3D map, and then search an optimal route through an algorithmic planning. However, for the tracking and shooting conditions of a known target image, the target and the environment are identified with a camera at the same time to plan a path, this has too much data calculation amount and makes a processor's resource allocation inadequate. For an auto-shooting mode with a very high real-time requirement, this method of planning the path is likely to cause the delay in tracking the device.